Rework TF trainer (huggingface#6038)

* Fully rework training/prediction loops

* fix method name

* Fix variable name

* Fix property name

* Fix scope

* Fix method name

* Fix tuple index

* Fix tuple index

* Fix indentation

* Fix variable name

* fix eval before log

* Add drop remainder for test dataset

* Fix step number + fix logging datetime

* fix eval loss value

* use global step instead of step + fix logging at step 0

* Fix logging datetime

* Fix global_step usage

* Fix breaking loop + logging datetime

* Fix step in prediction loop

* Fix step breaking

* Fix train/test loops

* Force TF at least 2.2 for the trainer

* Use assert_cardinality to facilitate the dataset size computation

* Log steps per epoch

* Make tfds compliant with TPU

* Make tfds compliant with TPU

* Use TF dataset enumerate instead of the Python one

* revert previous commit

* Fix data_dir

* Apply style

* rebase on master

* Address Sylvain's comments

* Address Sylvain's and Lysandre comments

* Trigger CI

* Remove unused import

examples/README.md

@@ -1,7 +1,7 @@
 # Examples
 
 Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
-Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.1+.
+Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
 
 Here is the list of all our examples:
 - **grouped by task** (all official examples work for multiple models)

examples/multiple-choice/utils_multiple_choice.py

@@ -204,6 +204,8 @@ def gen():
             )
 
         def get_dataset(self):
+            self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
+
             return self.dataset
 
         def __len__(self):

examples/question-answering/run_tf_squad.py

@@ -21,6 +21,8 @@
 from dataclasses import dataclass, field
 from typing import Optional
 
+import tensorflow as tf
+
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -68,6 +70,7 @@ class DataTrainingArguments:
     data_dir: Optional[str] = field(
         default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
     )
+    use_tfds: Optional[bool] = field(default=True, metadata={"help": "If TFDS should be used or not."})
     max_seq_length: int = field(
         default=128,
         metadata={
@@ -170,7 +173,7 @@ def main():
         )
 
     # Get datasets
-    if not data_args.data_dir:
+    if data_args.use_tfds:
         if data_args.version_2_with_negative:
             logger.warn("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")
 
@@ -179,7 +182,7 @@ def main():
         except ImportError:
             raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
 
-        tfds_examples = tfds.load("squad")
+        tfds_examples = tfds.load("squad", data_dir=data_args.data_dir)
         train_examples = (
             SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
             if training_args.do_train
@@ -209,6 +212,8 @@ def main():
         else None
     )
 
+    train_dataset = train_dataset.apply(tf.data.experimental.assert_cardinality(len(train_examples)))
+
     eval_dataset = (
         squad_convert_examples_to_features(
             examples=eval_examples,
@@ -223,6 +228,8 @@ def main():
         else None
     )
 
+    eval_dataset = eval_dataset.apply(tf.data.experimental.assert_cardinality(len(eval_examples)))
+
     # Initialize our Trainer
     trainer = TFTrainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset,)
 

examples/text-classification/run_tf_glue.py

@@ -9,6 +9,7 @@
 from typing import Dict, Optional
 
 import numpy as np
+import tensorflow as tf
 import tensorflow_datasets as tfds
 
 from transformers import (
@@ -35,7 +36,11 @@ class Split(Enum):
 
 
 def get_tfds(
-    task_name: str, tokenizer: PreTrainedTokenizer, max_seq_length: Optional[int] = None, mode: Split = Split.train
+    task_name: str,
+    tokenizer: PreTrainedTokenizer,
+    max_seq_length: Optional[int] = None,
+    mode: Split = Split.train,
+    data_dir: str = None,
 ):
     if task_name == "mnli-mm" and mode == Split.dev:
         tfds_name = "mnli_mismatched"
@@ -50,9 +55,11 @@ def get_tfds(
     else:
         tfds_name = task_name
 
-    ds = tfds.load("glue/" + tfds_name, split=mode.value)
+    ds, info = tfds.load("glue/" + tfds_name, split=mode.value, with_info=True, data_dir=data_dir)
+    ds = glue_convert_examples_to_features(ds, tokenizer, max_seq_length, task_name)
+    ds = ds.apply(tf.data.experimental.assert_cardinality(info.splits[mode.value].num_examples))
 
-    return glue_convert_examples_to_features(ds, tokenizer, max_seq_length, task_name)
+    return ds
 
 
 logger = logging.getLogger(__name__)
@@ -69,6 +76,7 @@ class GlueDataTrainingArguments:
     """
 
     task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
+    data_dir: Optional[str] = field(default=None, metadata={"help": "The input/output data dir for TFDS."})
     max_seq_length: int = field(
         default=128,
         metadata={
@@ -171,13 +179,22 @@ def main():
 
     # Get datasets
     train_dataset = (
-        get_tfds(task_name=data_args.task_name, tokenizer=tokenizer, max_seq_length=data_args.max_seq_length)
+        get_tfds(
+            task_name=data_args.task_name,
+            tokenizer=tokenizer,
+            max_seq_length=data_args.max_seq_length,
+            data_dir=data_args.data_dir,
+        )
         if training_args.do_train
         else None
     )
     eval_dataset = (
         get_tfds(
-            task_name=data_args.task_name, tokenizer=tokenizer, max_seq_length=data_args.max_seq_length, mode=Split.dev
+            task_name=data_args.task_name,
+            tokenizer=tokenizer,
+            max_seq_length=data_args.max_seq_length,
+            mode=Split.dev,
+            data_dir=data_args.data_dir,
         )
         if training_args.do_eval
         else None

examples/token-classification/run_tf_ner.py

@@ -17,7 +17,6 @@
 
 import logging
 import os
-import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
 
@@ -185,11 +184,6 @@ def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[L
 
         for i in range(batch_size):
             for j in range(seq_len):
-                if label_ids[i, j] == -1:
-                    label_ids[i, j] = -100
-                    warnings.warn(
-                        "Using `-1` to mask the loss for the token is depreciated. Please use `-100` instead."
-                    )
                 if label_ids[i, j] != -100:
                     out_label_list[i].append(label_map[label_ids[i][j]])
                     preds_list[i].append(label_map[preds[i][j]])

examples/token-classification/utils_ner.py

@@ -146,7 +146,7 @@ class TFNerDataset:
         """
 
         features: List[InputFeatures]
-        pad_token_label_id: int = -1
+        pad_token_label_id: int = -100
         # Use cross entropy ignore_index as padding label id so that only
         # real label ids contribute to the loss later.
 
@@ -221,6 +221,8 @@ def gen():
                 )
 
         def get_dataset(self):
+            self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
+
             return self.dataset
 
         def __len__(self):

src/transformers/modeling_tf_utils.py

@@ -17,7 +17,6 @@
 import functools
 import logging
 import os
-import warnings
 from typing import Dict, List, Optional, Union
 
 import h5py
@@ -174,11 +173,7 @@ def compute_loss(self, labels, logits):
         )
         # make sure only labels that are not equal to -100
         # are taken into account as loss
-        if tf.math.reduce_any(labels == -1).numpy() is True:
-            warnings.warn("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.")
-            active_loss = tf.reshape(labels, (-1,)) != -1
-        else:
-            active_loss = tf.reshape(labels, (-1,)) != -100
+        active_loss = tf.reshape(labels, (-1,)) != -100
         reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
         labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)